The aim of this research is to find geometric shape models of human organs. Specifically we look for three-dimensional models with a small number of parameters which can describe the normal shape of the organ and its pathological variations. An important part of the model is a mechanism for fitting the model to image data. The model-directed approach allows us to handle voluminous amounts of data and fit the data even when organ boundaries are obscure. The proposed work focuses on three specific projects. These projects have different medical relevancies, but are related in that their geometric shape models are similar. The cardiac ultrasound project will test our methodology for recognizing the borders of the left ventricle in three-dimensional, time-varying ultrasound data. Our goal is to make quantitative measurements of segmental wall motion abnormalities. The chest radiography project will finalize our method for detecting the borders of small nodular lung lesions. The main foci of this work will be the completion of automatic computer techniques for locating chest anatomy, and the demonstration of the value of the system in tests with radiologists and a lung lesion data base of approximately 150 films. The abdominal organ project will attempt to create three-dimensional displays of the boundaries of human organs from conventional computed tomography data. The aim of this project is to use organ displacements to study the extent of large abdominal tumors for treatment planning purposes.